Low power and Lossy Networks (LLNs), e.g., sensor networks, have a myriad of applications, such as Smart Grid and Smart Cities. Various challenges are presented with LLNs, such as lossy links, low bandwidth, battery operation, low memory and/or processing capability, etc. One example routing solution to LLN challenges is a protocol called Routing Protocol for LLNs or “RPL,” which is a distance vector routing protocol that builds a Destination Oriented Directed Acyclic Graph (DODAG, or simply DAG) in addition to a set of features to bound the control traffic, support local (and slow) repair, etc. The RPL architecture provides a flexible method by which each node performs DODAG discovery, construction, and maintenance.
Learning Machines (LM) are computational entities that rely on one or more machine learning algorithms for performing a task for which they have not been explicitly programmed to perform. In particular, they are capable of adjusting their behavior to their environment. In the context of LLNs, this ability can be very important, as the network often faces changing conditions and requirements that can be too large for being efficiently managed by a network operator.
Many LMs operate in a so-called batch mode, where a large dataset is collected, and the entire dataset is processed at once. However, batch learning algorithms can be very computation- and memory-intensive, and thus, are often not well-suited to real-time systems that need to quickly incorporate fresh incoming data, as are typically the case with devices at the edge, such as routers. As such, batch algorithms can be better suited for operation at a centralized network controller (e.g., in the cloud). On the other hand, other LMs operate in a so-called incremental mode, where the LM knowledge can be incremented with new data points at any time. Incremental learning algorithms are more lightweight and adaptable than their batch mode counterparts, and thus, are better suited for operation at the edge.